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The pathophysiological processes that cause Parkinson's disease (PD) affect dopamine neurons residing in the substantia nigra with devastating consequences for normal movement. One important gene involved in both familial and sporadic PD is alpha-synuclein. We have generated three strains of alpha-synuclein transgenic mice to study the pathologic consequences of the targeted expression of mutant or wild-type human alpha-synuclein in a model system. We have analyzed gene expression patterns in these mice using high throughput microarrays in anatomical regions implicated in disease (substantia nigra and brainstem). Our study reveals gene dosage-dependent dysregulation of several genes important for the dopaminergic phenotype in mice over-expressing wild-type human alpha-synuclein in the substantia nigra at time points preceding neuronal cell death. Analysis of mutant alpha-synuclein mice at a time point when pathology is advanced reveals several new candidate genes that may play a role in neuronal demise and/or protein accumulation.

Large-scale genomics approaches are now widely utilized to study a myriad of human diseases. These powerful techniques, when combined with data analysis tools, detect changes in transcript abundance in diseased tissue relative to control. We hypothesize that specific differential gene expression underlies important pathogenic processes in Parkinson's disease, which is characterized by the gradual loss of dopaminergic neurons in the substantia nigra and consequent loss of dopamine in the striatum. We have therefore examined gene expression levels in the human parkinsonian nigrostriatal pathway, and compared them with those of neurologically normal controls. Using unsupervised clustering methods, we demonstrate that relatively few genes' expression levels can effectively distinguish between disease and control brains. Further, we identify several interesting patterns of gene expression that illuminate pathogenic cascades in Parkinson's disease. In particular is the robust loss of synaptic gene expression in diseased substantia nigra and striatum.

Standard controls and best practice guidelines advance acceptance of data from research, preclinical and clinical laboratories by providing a means for evaluating data quality. The External RNA Controls Consortium (ERCC) is developing commonly agreed-upon and tested controls for use in expression assays, a true industry-wide standard control.

The gradual loss of striatal dopamine and dopaminergic neurons residing in the substantia nigra (SN) causes parkinsonism characterized by slow, halting movements, rigidity, and resting tremor when neuronal loss exceeds a threshold of approximately 80%. It is estimated that there is extensive compensation for several years prior to symptom onset, during which vulnerable neurons asynchronously die. Recent evidence would argue that much of the compensatory response of the nigrostriatal system is multimodal including both pre-synaptic and striatal mechanisms. Although parkinsonism may have multiple causes, the classic syndrome, Parkinson's disease (PD), is frequently modeled in small animals by repeated administration of the selective neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP). Because the MPTP model of PD recapitulates many of the known behavioral and pathological features of human PD, we asked whether the striatal cells of mice treated with MPTP in a semi-chronic paradigm enact a transcriptional program that would help elucidate the response to dopamine denervation. Our findings reveal a time-dependent dysregulation in the striatum of a set of genes whose products may impact both the viability and ability to communicate of dopamine neurons in the SN.

Parkinson's disease pathogenesis proceeds through several phases, culminating in the loss of dopaminergic neurons of the substantia nigra (SN). Although the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) model of oxidative SN injury is frequently used to study degeneration of dopaminergic neurons in mice and non-human primates, an understanding of the temporal sequence of molecular events from inhibition of mitochondrial complex 1 to neuronal cell death is limited. Here, microarray analysis and integrative data mining were used to uncover pathways implicated in the progression of changes in dopaminergic neurons after MPTP administration. This approach enabled the identification of small, yet consistently significant, changes in gene expression within the SN of MPTP-treated animals. Such an analysis disclosed dysregulation of genes in three main areas related to neuronal function: cytoskeletal stability and maintenance, synaptic integrity, and cell cycle and apoptosis. The discovery and validation of these alterations provide molecular evidence for an evolving cascade of injury, dysfunction, and cell death.

Background Despite the widespread use of microarrays, much ambiguity regarding data analysis, interpretation and correlation of the different technologies exists. There is a considerable amount of interest in correlating results obtained between different microarray platforms. To date, only a few cross-platform evaluations have been published and unfortunately, no guidelines have been established on the best methods of making such correlations. To address this issue we conducted a thorough evaluation of two commercial microarray platforms to determine an appropriate methodology for making cross-platform correlations. Results In this study, expression measurements for 10,763 genes uniquely represented on Affymetrix U133A/B GeneChips® and Amersham CodeLink™ UniSet Human 20 K microarrays were compared. For each microarray platform, five technical replicates, derived from the same total RNA samples, were labeled, hybridized, and quantified according to each manufacturers' standard protocols. The correlation coefficient (r) of differential expression ratios for the entire set of 10,763 overlapping genes was 0.62 between platforms. However, the correlation improved significantly (r = 0.79) when genes within noise were excluded. In addition to levels of inter-platform correlation, we evaluated precision, statistical-significance profiles, power, and noise levels for each microarray platform. Accuracy of differential expression was measured against real-time PCR for 25 genes and both platforms correlated well with r values of 0.92 and 0.79 for CodeLink and GeneChip, respectively. Conclusions As a result of this study, we recommend using only genes called 'present' in cross-platform correlations. However, as in this study, a large number of genes may be lost from the correlation due to differing levels of noise between platforms. This is an important consideration given the apparent difference in sensitivity of the two platforms. Data from microarray analysis need to be interpreted cautiously and therefore, we provide guidelines for making cross-platform correlations. In all, this study represents the most comprehensive and specifically designed comparison of short-oligonucleotide microarray platforms to date using the largest set of overlapping genes.

The comparability and reliability of data generated using microarray technology would be enhanced by use of a common set of standards that allow accuracy, reproducibility and dynamic range assessments on multiple formats. We designed and tested a complex biological reagent for performance measurements on three commercial oligonucleotide array formats that differ in probe design and signal measurement methodology. The reagent is a set of two mixtures with different proportions of RNA for each of four rat tissues (brain, liver, kidney and testes). The design provides four known ratio measurements of >200 reference probes, which were chosen for their tissue-selectivity, dynamic range coverage and alignment to the same exemplar transcript sequence across all three platforms. The data generated from testing three biological replicates of the reagent at eight laboratories on three array formats provides a benchmark set for both laboratory and data processing performance assessments. Close agreement with target ratios adjusted for sample complexity was achieved on all platforms and low variance was observed among platforms, replicates and sites. The mixed tissue design produces a reagent with known gene expression changes within a complex sample and can serve as a paradigm for performance standards for microarrays that target other species.